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Liu, Pan

Pricing strategies of a three-stage supply chain: a new research in the big data era. (English) Zbl 1412.90024

Discrete Dyn. Nat. Soc. 2017, Article ID 9024712, 16 p. (2017).
Summary: In the big data era, data company as the big data information (BDI) supplier should be included in a supply chain. In the new situation, to research the pricing strategies of supply chain, a three-stage supply chain with one manufacturer, one retailer, and one data company was chosen. Meanwhile, considering the manufacturer contained the internal and external BDI, four benefit models about BDI investment were proposed and analyzed in both decentralized and centralized supply chain using Stackelberg game. Meanwhile, the optimal retail price and benefits in the four models were compared. Findings are as follows. (1) The industry cost improvement coefficient, the internal BDI investment cost of the manufacturer, and the added cost of the data company on using big data technology have different relationships with the optimal prices of supply chain members in different models. (2) In the retailer-dominated supply chain model, the optimal benefits of the retailer and the manufacturer are the same, and the optimal benefits of the data company are biggest in all the members.

Cited in 1 Document

MSC:

90B06 Transportation, logistics and supply chain management
91B24 Microeconomic theory (price theory and economic markets)

Keywords:

pricing strategies; three-stage supply chain; big data
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[1] Cambria, E.; Mazzocco, T.; Hussain, A., Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining, Biologically Inspired Cognitive Architectures, 4, 41-53, (2013) · doi:10.1016/j.bica.2013.02.003
[2] Huang, X.-B.; Zhong, H.-X., Build enterprise competitive intelligence system model based on Big Data, Journal of Intelligence, 03, 37-43, (2013)
[3] Sanders, N. R., Big Data Driven Supply Chain Management, (2015), China Ren Min University Press
[4] Barton, D., Making advanced analytics work for you, Harvard Business Review, 38, 90, 78-128, (2012)
[5] McAfee, A., Big data: the management revolution, Harvard Business Review, 10, 90, 68-128, (2012)
[6] Chae, B. K.; Olson, D. L., Business analytics for supply chain: a dynamic-capabilities framework, International Journal of Information Technology & Decision Making, 12, 1, 9-26, (2013) · doi:10.1142/S0219622013500016
[7] Hazen, B. T.; Boone, C. A.; Ezell, J. D.; Jones-Farmer, L. A., Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications, International Journal of Production Economics, 154, 72-80, (2014) · doi:10.1016/j.ijpe.2014.04.018
[8] Trkman, P.; McCormack, K.; De Oliveira, M. P. V.; Ladeira, M. B., The impact of business analytics on supply chain performance, Decision Support Systems, 49, 3, 318-327, (2010) · doi:10.1016/j.dss.2010.03.007
[9] Waller, M. A.; Fawcett, S. E., Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management, Journal of Business Logistics, 34, 2, 77-84, (2013) · doi:10.1111/jbl.12010
[10] Dutta, D.; Bose, I., Managing a big data project: The case of Ramco cements limited, International Journal of Production Economics, 165, article no. 5962, 293-306, (2015) · doi:10.1016/j.ijpe.2014.12.032
[11] Tan, K. H.; Zhan, Y. Z.; Ji, G.; Ye, F.; Chang, C., Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph, International Journal of Production Economics, 165, 223-233, (2015) · doi:10.1016/j.ijpe.2014.12.034
[12] Bi, Z.; Cochran, D., Big data analytics with applications, Journal of Management Analytics, 1, 4, 249-265, (2015) · doi:10.1080/23270012.2014.992985
[13] Fosso Wamba, S.; Akter, S.; Edwards, A.; Chopin, G.; Gnanzou, D., How ’big data’ can make big impact: Findings from a systematic review and a longitudinal case study, International Journal of Production Economics, 165, article no. 5961, 234-246, (2015) · doi:10.1016/j.ijpe.2014.12.031
[14] Janssen, M.; van der Voort, H.; Wahyudi, A., Factors influencing big data decision-making quality, Journal of Business Research, 70, 338-345, (2017) · doi:10.1016/j.jbusres.2016.08.007
[15] Wamba, S. F.; Gunasekaran, A.; Akter, S.; Ren, S. J.-F.; Dubey, R.; Childe, S. J., Big data analytics and firm performance: Effects of dynamic capabilities, Journal of Business Research, 70, 356-365, (2017) · doi:10.1016/j.jbusres.2016.08.009
[16] Gunasekaran, A.; Papadopoulos, T.; Dubey, R.; Wamba, S. F.; Childe, S. J.; Hazen, B.; Akter, S., Big data and predictive analytics for supply chain and organizational performance, Journal of Business Research, (2016) · doi:10.1016/j.jbusres.2016.08.004
[17] Akter, S.; Wamba, S. F.; Gunasekaran, A.; Dubey, R.; Childe, S. J., How to improve firm performance using big data analytics capability and business strategy alignment?, International Journal of Production Economics, 182, 113-131, (2016) · doi:10.1016/j.ijpe.2016.08.018
[18] Hofmann, E., Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect, International Journal of Production Research, 1-19, (2017) · doi:10.1080/00207543.2015.1061222
[19] Brinch, M.; Stentoft, J.; Jensen, J. K., Big Data and its Applications in Supply Chain Management: Findings from a Delphi Study, Proceedings of the Hawaii International Conference on System Sciences
[20] Lamba, K.; Singh, S. P., Big data in operations and supply chain management: current trends and future perspectives, Production Planning and Control, 28, 877-890, (2017)
[21] Niu, B.; Zou, Z., Better demand signal, better decisions? evaluation of big data in a licensed remanufacturing supply chain with environmental risk considerations, Risk Analysis, 3, (2017) · doi:10.1111/risa.12796
[22] Chavez, R.; Yu, W.; Jacobs, M. A., Data-driven supply chains, manufacturing capability and customer satisfaction, Production Planning and Control, 28, 11-12, (2017)
[23] Ji, G.; Hu, L.; Tan, K. H., A study on decision-making of food supply chain based on big data, Journal of Systems Science and Systems Engineering, 26, 2, 1-16, (2017)
[24] Badiezadeh, T.; Saen, R. F.; Samavati, T., Assessing sustainability of supply chains by double frontier network DEA: A big data approach, Computers Operations Research, 1-22, (2017) · Zbl 1391.90340
[25] Mukherjee, U. K.; Sinha, K. K., Product recall decisions in medical device supply chains: a big data analytic approach to evaluating judgment bias, Production Engineering Research and Development, 1-20, (2017)
[26] Mani, V.; Delgado, C.; Hazen, B.; Patel, P., Mitigating supply chain risk via sustainability using big data analytics: evidence from the manufacturing supply chain, Sustainability, 608, 9, 1-21, (2017)
[27] Yu, W.; Chavez, R.; Jacobs, M. A., Data-driven supply chain capabilities and performance: A resource-based view, Transportation Research Part E Logistics Transportation Review, 1-24, (2017)
[28] Olama, M. M.; McNair, A. W.; Sukumar, S. R.; Nutaro, J. J., A qualitative readiness-requirements assessment model for enterprise big-data infrastructure investment, International Society for Optics and Photonics, 9122, (2014)
[29] Tambe, P., Big data investment, skills, and firm value, Management Science, 60, 6, 1452-1469, (2014) · doi:10.1287/mnsc.2014.1899
[30] Shi, H.; Li, J., effect of private information leakage on competition relationship among supply chain enterprises in big data era[J], Journal of the China Society for Scientific andTechnical Information, 1, 53-65, (2015)
[31] Wu, C.-X.; Zhao, D.-Z.; Pan, X.-Y., Comparison on dynamic cooperation strategies of a three-echelon supply chain involving big data service provider, Control and Decision, 31, 7, 1169-1177, (2016) · Zbl 1374.90030 · doi:10.13195/j.kzyjc.2015.0700
[32] Liu, P.; Yi, S.-P., A study on supply chain investment decision-making and coordination in the Big Data environment, Annals of Operations Research, 1-19, (2017) · Zbl 1411.90029 · doi:10.1007/s10479-017-2424-4
[33] Liu, P.; Yi, S.-p., Investment decision-making and coordination of supply chain: a new research in the big data era, Discrete Dynamics in Nature and Society, (2016) · Zbl 1417.90088 · doi:10.1155/2016/2026715
[34] Baker, W.; Kiewell, D., Using Big Data to Make Better Pricing Decisions, 27, (2014), McKinsey and Company
[35] Liu, P.; Yi, S., Pricing policies of green supply chain considering targeted advertising and product green degree in the Big Data environment, Journal of Cleaner Production, 164, 1614-1622, (2017) · doi:10.1016/j.jclepro.2017.07.049
[36] Li, G.; Mao, H.; Xiao, L., Impacts of leader-follower structure on pricing and production strategies in a decentralized assembly system, Asia-Pacific Journal of Operational Research, 34, 1, (2017) · Zbl 1361.90020 · doi:10.1142/S0217595917400036
[37] Ferrer, G.; Swaminathan, J. M., Managing new and remanufactured products, Management Science, 52, 1, 15-26, (2006) · doi:10.1287/mnsc.1050.0465
[38] Gao, P.; Wang, X.; Jing, Y.; Deng, L., Differential price strategies between remanufactured products with refurbished products based on heterogeneous demands, Computer Integrated Manufacturing Systems, 20, 9, 2134-2145, (2014) · doi:10.13196/j.cims.2014.09.008
[39] Hernandez, M. A., Nonlinear pricing and competition intensity in a Hotelling-type model with discrete product and consumer types, Economics Letters, 110, 3, 174-177, (2011) · Zbl 1210.91038 · doi:10.1016/j.econlet.2010.11.031
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